Continual Learning:
Upon detecting novel attack data points, the base model needs to be continually updated to gain the ability to recognize the newly identified attack types in the future.
Recent works of CL [1]} for deep neural networks have mostly focused on tackling catastrophic forgetting [2]}. It occurs when a deep neural network is updated to learn drifts in data distribution in a CL setting which would lead to underperformance on the past learned tasks.
